- AI training models rely heavily on scraping vast datasets from the internet, raising severe privacy concerns regarding personal data consumption. - Memorization and inversion attacks allow malicious actors to extract training data, including personally identifiable information (PII), from trained neural networks. - Current regulatory frameworks struggle to balance rapid technological advancement in machine learning with robust data privacy safeguards. - There is an urgent need for privacy-preserving machine learning techniques such as differential privacy and federated learning. - **Digital Personal Data Protection Act:** India's comprehensive legislative framework for regulating the processing of digital personal data, balancing individual privacy rights with the lawful needs of data fiduciaries. **Introduction** The article explores how artificial intelligence training models pose significant challenges to individual privacy. It highlights how machine learning models ingest large-scale web data, retain personal details through memorization, and expose vulnerabilities to data extraction attacks, demanding stricter regulatory frameworks and privacy-centric architectures. **Core Issue and Detailed Points** - AI training models rely heavily on scraping vast datasets from the internet, raising severe privacy concerns regarding personal data consumption. - Memorization and inversion attacks allow malicious actors to extract training data, including personally identifiable information (PII), from trained neural networks. - Current regulatory frameworks struggle to balance rapid technological advancement in machine learning with robust data privacy safeguards. - There is an urgent need for privacy-preserving machine learning techniques such as differential privacy and federated learning. **Key Statutory Bodies, Acts & Concepts** - **Digital Personal Data Protection Act:** India's comprehensive legislative framework for regulating the processing of digital personal data, balancing individual privacy rights with the lawful needs of data fiduciaries.